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【开源实习】香橙派github仓中的在线推理案例08-LSTM+CRF基于PyNative模式改写成以mint接口实现，任务编号ID4HKE #115

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【开源实习】香橙派github仓中的在线推理案例08-LSTM+CRF基于PyNative模式改写成以mint接口实现，任务编号ID4HKE #115

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156 changes: 95 additions & 61 deletions Online/inference/08-LSTM+CRF/mindspore_sequence_labeling.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -48,13 +48,42 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 1,
"id": "f01741d9",
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages/numpy/core/getlimits.py:549: UserWarning: The value of the smallest subnormal for <class 'numpy.float64'> type is zero.\n",
" setattr(self, word, getattr(machar, word).flat[0])\n",
"/home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for <class 'numpy.float64'> type is zero.\n",
" return self._float_to_str(self.smallest_subnormal)\n",
"/home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages/numpy/core/getlimits.py:549: UserWarning: The value of the smallest subnormal for <class 'numpy.float32'> type is zero.\n",
" setattr(self, word, getattr(machar, word).flat[0])\n",
"/home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for <class 'numpy.float32'> type is zero.\n",
" return self._float_to_str(self.smallest_subnormal)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.6.0\n"
]
}
],
"source": [
"import mindspore\n",
"mindspore.set_context(max_device_memory=\"2GB\", mode=mindspore.GRAPH_MODE, device_target=\"Ascend\", jit_config={\"jit_level\":\"O2\"}, ascend_config={\"precision_mode\":\"allow_mix_precision\"})"
"import mindspore as ms\n",
"import mindspore.nn as nn\n",
"import mindspore.ops as ops\n",
"import mindspore.numpy as mnp\n",
"from mindspore.common.initializer import initializer, Uniform\n",
"import mindspore.mint as mint\n",
"mindspore.set_context(max_device_memory=\"2GB\", mode=mindspore.PYNATIVE_MODE, device_target=\"Ascend\", jit_config={\"jit_level\":\"O0\"}, ascend_config={\"precision_mode\":\"allow_mix_precision\"})\n",
"print(mindspore.__version__)\n"
]
},
{
Expand Down Expand Up @@ -124,7 +153,7 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 2,
"id": "d09936ad-6b20-4423-9f57-8e14917e61d4",
"metadata": {},
"outputs": [],
Expand All @@ -142,7 +171,7 @@
" score = start_trans[tags[0]]\n",
" # score += 第一次发射概率\n",
" # shape: (batch_size,)\n",
" score += emissions[0, mnp.arange(batch_size), tags[0]]\n",
" score += emissions[0, mint.arange(batch_size), tags[0]]\n",
"\n",
" for i in range(1, seq_length):\n",
" # 标签由i-1转移至i的转移概率（当mask == 1时有效）\n",
Expand All @@ -151,11 +180,11 @@
"\n",
" # 预测tags[i]的发射概率（当mask == 1时有效）\n",
" # shape: (batch_size,)\n",
" score += emissions[i, mnp.arange(batch_size), tags[i]] * mask[i]\n",
" score += emissions[i, mint.arange(batch_size), tags[i]] * mask[i]\n",
"\n",
" # 结束转移\n",
" # shape: (batch_size,)\n",
" last_tags = tags[seq_ends, mnp.arange(batch_size)]\n",
" last_tags = tags[seq_ends, mint.arange(batch_size)]\n",
" # score += 结束转移概率\n",
" # shape: (batch_size,)\n",
" score += end_trans[last_tags]\n",
Expand Down Expand Up @@ -185,7 +214,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 3,
"id": "d9a0ef6a-1c3a-400e-9053-e0659e8f9e7e",
"metadata": {},
"outputs": [],
Expand Down Expand Up @@ -217,18 +246,18 @@
"\n",
" # 对score_i做log_sum_exp运算，用于下一个Token的score计算\n",
" # shape: (batch_size, num_tags)\n",
" next_score = ops.logsumexp(next_score, axis=1)\n",
" next_score = mint.logsumexp(next_score, dim=1)\n",
"\n",
" # 当mask == 1时，score才会变化\n",
" # shape: (batch_size, num_tags)\n",
" score = mnp.where(mask[i].expand_dims(1), next_score, score)\n",
" score = mint.where(mask[i].expand_dims(1), next_score, score)\n",
"\n",
" # 最后加结束转移概率\n",
" # shape: (batch_size, num_tags)\n",
" score += end_trans\n",
" # 对所有可能的路径得分求log_sum_exp\n",
" # shape: (batch_size,)\n",
" return ops.logsumexp(score, axis=1)"
" return mint.logsumexp(score, dim=1)"
]
},
{
Expand All @@ -251,7 +280,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 4,
"id": "7c286935-74eb-413a-8e47-b8fb5264e6b6",
"metadata": {},
"outputs": [],
Expand All @@ -262,20 +291,22 @@
"\n",
" seq_length = mask.shape[0]\n",
"\n",
" score = start_trans + emissions[0]\n",
" score = (start_trans + emissions[0]).astype(ms.float32)\n",
" history = ()\n",
"\n",
" mask_bool = mask.astype(ms.uint8)\n",
" for i in range(1, seq_length):\n",
" broadcast_score = score.expand_dims(2)\n",
" broadcast_emission = emissions[i].expand_dims(1)\n",
" next_score = broadcast_score + trans + broadcast_emission\n",
"\n",
" # 求当前Token对应score取值最大的标签，并保存\n",
" #indices = next_score.argmax(axis=1)\n",
" indices = next_score.argmax(axis=1)\n",
" history += (indices,)\n",
"\n",
" next_score = next_score.max(axis=1)\n",
" score = mnp.where(mask[i].expand_dims(1), next_score, score)\n",
" score = mint.where(mask_bool[i].expand_dims(1), next_score, score)\n",
"\n",
"\n",
" score += end_trans\n",
"\n",
Expand Down Expand Up @@ -321,37 +352,15 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 5,
"id": "120a39c7-c89d-4cd3-8a75-da27d2853f0e",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[WARNING] ME(97316:281472950050832,MainProcess):2024-09-06-10:50:02.452.307 [mindspore/run_check/_check_version.py:357] MindSpore version 2.2.14 and Ascend AI software package (Ascend Data Center Solution)version 7.0 does not match, the version of software package expect one of ['7.1']. Please refer to the match info on: https://www.mindspore.cn/install\n",
"/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/numpy/core/getlimits.py:549: UserWarning: The value of the smallest subnormal for <class 'numpy.float64'> type is zero.\n",
" setattr(self, word, getattr(machar, word).flat[0])\n",
"/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/numpy/core/getlimits.py:549: UserWarning: The value of the smallest subnormal for <class 'numpy.float32'> type is zero.\n",
" setattr(self, word, getattr(machar, word).flat[0])\n",
"[WARNING] ME(97316:281472950050832,MainProcess):2024-09-06-10:50:04.855.681 [mindspore/run_check/_check_version.py:375] MindSpore version 2.2.14 and \"te\" wheel package version 7.0 does not match. For details, refer to the installation guidelines: https://www.mindspore.cn/install\n",
"[WARNING] ME(97316:281472950050832,MainProcess):2024-09-06-10:50:04.861.443 [mindspore/run_check/_check_version.py:382] MindSpore version 2.2.14 and \"hccl\" wheel package version 7.0 does not match. For details, refer to the installation guidelines: https://www.mindspore.cn/install\n",
"[WARNING] ME(97316:281472950050832,MainProcess):2024-09-06-10:50:04.862.319 [mindspore/run_check/_check_version.py:396] Please pay attention to the above warning, countdown: 3\n",
"[WARNING] ME(97316:281472950050832,MainProcess):2024-09-06-10:50:05.864.215 [mindspore/run_check/_check_version.py:396] Please pay attention to the above warning, countdown: 2\n",
"[WARNING] ME(97316:281472950050832,MainProcess):2024-09-06-10:50:06.866.175 [mindspore/run_check/_check_version.py:396] Please pay attention to the above warning, countdown: 1\n"
]
}
],
"outputs": [],
"source": [
"import mindspore as ms\n",
"import mindspore.nn as nn\n",
"import mindspore.ops as ops\n",
"import mindspore.numpy as mnp\n",
"from mindspore.common.initializer import initializer, Uniform\n",
"\n",
"def sequence_mask(seq_length, max_length, batch_first=False):\n",
" \"\"\"根据序列实际长度和最大长度生成mask矩阵\"\"\"\n",
" range_vector = mnp.arange(0, max_length, 1, seq_length.dtype)\n",
" range_vector = mint.arange(0, max_length, 1, dtype=seq_length.dtype)\n",
" result = range_vector < seq_length.view(seq_length.shape + (1,))\n",
" if batch_first:\n",
" return result.astype(ms.int64)\n",
Expand Down Expand Up @@ -385,7 +394,7 @@
" max_length, batch_size = tags.shape\n",
"\n",
" if seq_length is None:\n",
" seq_length = mnp.full((batch_size,), max_length, ms.int64)\n",
" seq_length = mint.full((batch_size,), max_length, ms.int64)\n",
"\n",
" mask = sequence_mask(seq_length, max_length)\n",
"\n",
Expand All @@ -412,7 +421,7 @@
" batch_size, max_length = emissions.shape[:2]\n",
"\n",
" if seq_length is None:\n",
" seq_length = mnp.full((batch_size,), max_length, ms.int64)\n",
" seq_length = mint.full((batch_size,), max_length, ms.int64)\n",
"\n",
" mask = sequence_mask(seq_length, max_length)\n",
"\n",
Expand All @@ -437,7 +446,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 6,
"id": "c07555e3-f2a2-4c25-beff-5a78491ab2d1",
"metadata": {},
"outputs": [],
Expand Down Expand Up @@ -469,7 +478,7 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 7,
"id": "fa53f535-34bc-49a3-b769-e85d3a184cc0",
"metadata": {},
"outputs": [],
Expand Down Expand Up @@ -497,7 +506,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 8,
"id": "5c255358-938b-4e88-a810-2fe4e616a044",
"metadata": {},
"outputs": [
Expand All @@ -507,7 +516,7 @@
"21"
]
},
"execution_count": 7,
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
Expand All @@ -526,7 +535,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 9,
"id": "e6d3f2bb-0ea7-457c-8a74-a8e0ea78a109",
"metadata": {},
"outputs": [],
Expand All @@ -544,7 +553,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 10,
"id": "7899b6e6-95a4-4ffe-ba49-b27cebe8b306",
"metadata": {},
"outputs": [],
Expand All @@ -569,7 +578,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 11,
"id": "0e984dcb-6f89-4520-940f-620cdb997529",
"metadata": {},
"outputs": [
Expand All @@ -579,7 +588,7 @@
"((2, 11), (2, 11), (2,))"
]
},
"execution_count": 10,
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
Expand All @@ -599,10 +608,33 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 12,
"id": "65304086-de03-4edc-b3a2-3ca5e43c4795",
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading data from https://cdn.modelers.cn/lfs/b8/d8/c8dd957c842f8c2c4c07ee2e77dee1fca7abe0e8305fdc6b301453aae00b?response-content-disposition=attachment%3B+filename%3D%22lstm-crf.ckpt%22&AWSAccessKeyId=HAZQA0Q6AQL2GHX4TKTL&Expires=1761968039&Signature=eI%2FfqpDCWGkcGche5vPhitwqZik%3D (19 kB)\n",
"\n",
"file_sizes: 100%|██████████████████████████| 19.8k/19.8k [00:00<00:00, 10.8MB/s]\n",
"Successfully downloaded file to ./lstm-crf.ckpt\n"
]
},
{
"data": {
"text/plain": [
"Tensor(shape=[2, 3], dtype=Float32, value=\n",
"[[ 3.28305664e+01, 3.76408653e+01, 3.22400093e+01],\n",
" [ 2.89628849e+01, 2.69458580e+01, 3.40970802e+01]])"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from mindspore import load_param_into_net, load_checkpoint\n",
"from download import download\n",
Expand All @@ -628,7 +660,7 @@
},
{
"cell_type": "code",
"execution_count": 20,
"execution_count": 13,
"id": "ed5ff78a-099c-41f8-ac1e-8f9fbfab7659",
"metadata": {},
"outputs": [
Expand All @@ -638,7 +670,7 @@
"[[0, 1, 1, 1, 2, 2, 2, 2, 2, 0, 1], [0, 1, 2, 2, 2, 2, 2, 2, 2]]"
]
},
"execution_count": 20,
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
Expand All @@ -658,7 +690,7 @@
},
{
"cell_type": "code",
"execution_count": 21,
"execution_count": 14,
"id": "df505983-9c1f-4e09-8a67-1cd78ce69fd0",
"metadata": {},
"outputs": [],
Expand All @@ -674,9 +706,11 @@
},
{
"cell_type": "code",
"execution_count": 22,
"execution_count": 15,
"id": "e30d43c7-7c09-445e-94a4-33fccfaeaf11",
"metadata": {},
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
Expand All @@ -685,7 +719,7 @@
" ['B', 'I', 'O', 'O', 'O', 'O', 'O', 'O', 'O']]"
]
},
"execution_count": 22,
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
Expand All @@ -697,9 +731,9 @@
],
"metadata": {
"kernelspec": {
"display_name": "MindSpore",
"display_name": "jupyter",
"language": "python",
"name": "mindspore"
"name": "jupyter"
},
"language_info": {
"codemirror_mode": {
Expand All @@ -711,7 +745,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.18"
"version": "3.9.23"
},
"vscode": {
"interpreter": {
Expand Down